文献类型：期刊文献

英文题名：Online Performance Monitoring and Modeling Paradigm Based on Just-in-Time Learning and Extreme Learning Machine for a Non-Gaussian Chemical Process

作者：Peng, Xin[1];Tang, Yang[1];Du, Wenli[1];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai, Peoples R China

年份：2017

卷号：56

期号：23

起止页码：6671

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20172803903218);WOS:【SCI-EXPANDED(收录号:WOS:000403631000015)】；

基金：This work was supported by National Natural Science Foundation of China (61590923, 61422303, 61333010), and "Shu Guang" project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation.

语种：英文

外文关键词：Gaussian noise (electronic) - Just in time production - Learning systems - E-learning - Gaussian distribution - Knowledge acquisition

摘要：A novel performance monitoring and online modeling method to deal with a non-Gaussian chemical process with multiple operating conditions is proposed. On the basis of the framework of the proposed method, a kernel extreme learning machine (ELM) technique is used to efficiently extract features from high dimensional process data. Additionally, the Fastfood kernel is introduced into kernel ELM to accelerate computation efficiency, which is relatively low at the prediction time. Then, a modified just-in-time learning (JITL) technique is applied for Online modeling. In JITL, a novel similarity index; called modified adjusted cosine similarity (MACS), is proposed so as to improve the prediction performance of online modeling. The proposed paradigm provides an efficient, accurate, and fast approach to. monitor and model the multimode chemical process. The validity and effectiveness are evaluated by applying the method to a synthetic non-Gaussian multimode model and the distillation system.